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Estimation quality and required sample sizes in three-level contextual analysis models

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2023

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Methodology. Leibniz Institute for Psychology Information (ZPID). 2023, 19(2), pp. 133-151. ISSN 1614-1881. eISSN 1614-2241. Available under: doi: 10.5964/meth.9775

Zusammenfassung

In multilevel analysis, Level-1 predictors that also explain variance at a higher level are called contextual predictors. In the multilevel manifest covariate model, the Level-2 component is modeled as the average of the Level-1 predictor scores within a cluster. In the multilevel latent covariate model, the predictor is decomposed into two latent variables at Level-1 and Level-2. Performance conditions of these modeling approaches for three-level models are largely unexplored. We investigate the two approaches’ performance with respect to bias, coverage, and power in a three-level random intercept model. Results reveal differences in estimation quality and required sample sizes. We provide sampling recommendations for both approaches.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
150 Psychologie

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hierarchical linear model, three-level model, sample sizes, parameter estimation bias, power, coverage, contextual variable

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ISO 690KERKHOFF, Denny, Fridtjof W. NUSSBECK, 2023. Estimation quality and required sample sizes in three-level contextual analysis models. In: Methodology. Leibniz Institute for Psychology Information (ZPID). 2023, 19(2), pp. 133-151. ISSN 1614-1881. eISSN 1614-2241. Available under: doi: 10.5964/meth.9775
BibTex
@article{Kerkhoff2023-06-30Estim-67529,
  year={2023},
  doi={10.5964/meth.9775},
  title={Estimation quality and required sample sizes in three-level contextual analysis models},
  number={2},
  volume={19},
  issn={1614-1881},
  journal={Methodology},
  pages={133--151},
  author={Kerkhoff, Denny and Nussbeck, Fridtjof W.}
}
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